Low-probability events detection using unsupervised multiprototype clustering for single-molecule electronics  

作　　者：Chi Shang Rigong Te Shenglun Xiong Xipeng Liu Taige Lu Yixuan Zhu Chun Tang Jing Li Yu Zhou Haojie Liu Junyang Liu Wenjing Hong 

机构地区：[1]Institution State Key Laboratory of Physical Chemistry of Solid Surfaces,College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM),Xiamen University,Xiamen 361005,China [2]Research Center of Grid Energy Storage and Battery Application,School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China

出　　处：《Nano Research》2025年第4期402-410,共9页纳米研究(英文版)

基　　金：supported by the National Key Research and Development Program of China(No.2024YFA1208103);the National Natural Science Foundation of China(Nos.22403079,22173075,22325303,21933012,and 22250003);the Fujian Provincial Department of Science and Technology(Nos.2022H6014 and 2023H6002);the Fundamental Research Funds for the Central Universities(Nos.20720220020 and 20720200068).

摘　　要：Artificial intelligence for science(AI4S)has emerged as a new horizon in state-of-the-art scientific research,and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in correlating highthroughput and high-quality data with clear physical mechanisms.Towards using artificial intelligence for single-molecule electronics(AI4SME),the unsupervised extraction of lowprobability events from the massive experimental data becomes the key step,which has emerged for accurate detection of different configurations and even structural changes in singlemolecule junctions.However,the present algorithms suffer from the“uniform effect”,in which the majority events are erroneously allocated to minority ones,resulting in a relatively equal spread of cluster sizes and hindering the investigations for charge transport mechanisms with subtle and complex behaviors in single-molecule electronics.In this work,we propose a new multi-prototype clustering technique for precisely discriminating molecular events during the break junction process,especially those occurring with a probability below 10%,and further precisely extract the product species at the onset of the electric field-driven single-molecule keto-enol reaction with a probability as low as 1.5%.Our work tackles the long-term bottleneck of uniform effect for the precise detection of low-probability single-molecule events.

关 键 词：single-molecule electronics low-probability events detection machine learning artificial intelligence for science(AI4S) 

分 类 号：O641[理学—物理化学]